Multi-scale local-global architecture for person re-identification

Jing, Liu; Tiwari, Prayag; Nguyen, Thi Thanh Huyen; Gupta, Deepak; Band, Shahab S.

doi:10.1007/s00500-022-06859-6

Cited by 3 publications

(1 citation statement)

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“…Table 2 and Table 3 shows that our proposed method has achieved comparable performance to some of the state-of-the-art methods developed in recent years. [17] 88.70 --78.40 ACMMM2018 IANet [41] 87.10 --73.40 CVPR19 CAR [42] 86.30 --73.10 ICCV19 SAN [43] 87.90 --75.50 AAAI20 DLBC [44] 88.70 94.90 96.60 78.50 ACM MM20 GPS [45] 88.20 95.20 96.70 78.70 CVPR 2021 MSLG [46] 82.00 83.00 91.00 65.00 Soft Computing 2022 CASN+PCB [47] 87.70 --73.70 CVPR19 DCDS + Re-rank [48]…”

Section: Resultsmentioning

confidence: 99%

Global-local attention with triplet loss and label smoothed crossentropy for person re-identification

Tran,

Nguyen,

Nguyen

et al. 2023

IJ-AI

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Person re-identification (Person Re-ID) is a research direction on tracking and identifying people in surveillance camera systems with non-overlapping camera perspectives. Despite much research on this topic, there are still some practical problems that Person Re-ID has not yet solved, in reality, human objects can easily be obscured by obstructions such as other people, trees, luggage, umbrellas, signs, cars, motorbikes. In this paper, we propose a multibranch deep learning network architecture. In which one branch is for the representation of global features and two branches are for the representation of local features. Dividing the input image into small parts and changing the number of parts between the two branches helps the model to represent the features better. In addition, we add an attention module to the ResNet50 backbone that enhances important human characteristics and eliminates irrelevant information. To improve robustness, the model is trained by combining triplet loss and label smoothing cross-entropy loss (LSCE). Experiments are carried out on datasets Market1501, and duke multi-target multi-camera (DukeMTMC) datasets, our method achieved 96.04% rank-1, 88,11% mean average precision (mAP) on the Market1501 dataset, and 88.78% rank-1, 78,6% mAP on the DukeMTMC dataset. This method achieves performance better than some state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Global-local attention with triplet loss and label smoothed crossentropy for person re-identification

Tran,

Nguyen,

Nguyen

et al. 2023

IJ-AI

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show abstract

Boosting person ReID feature extraction via dynamic convolution

Akbaba,

Gurkan,

Gunsel

2024

Pattern Anal Applic

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Extraction of discriminative features is crucial in person re-identification (ReID) which aims to match a query image of a person to her/his images, captured by different cameras. The conventional deep feature extraction methods on ReID employ CNNs with static convolutional kernels, where the kernel parameters are optimized during the training and remain constant in the inference. This approach limits the network's ability to model complex contents and decreases performance, particularly when dealing with occlusions or pose changes. In this work, to improve the performance without a significant increase in parameter size, we present a novel approach by utilizing a channel fusion-based dynamic convolution backbone network, which enables the kernels to change adaptively based on the input image, within two existing ReID network architectures. We replace the backbone network of two ReID methods to investigate the effect of dynamic convolution on both simple and complex networks. The first one called Baseline, is a simpler network with fewer layers, while the second, CaceNet represents a more complex architecture with higher performance. Evaluation results demonstrate that both of the designed dynamic networks improve identification accuracy compared to the static counterparts. A significant increase in accuracy is reported under occlusion tested on Occluded-DukeMTMC. Moreover, our approach achieves a performance comparable to the state-of-the-art on Market1501, DukeMTMC-reID, and CUHK03 with a limited computational load. These findings validate the effectiveness of the dynamic convolution in enhancing the person ReID networks and push the boundaries of performance in this domain.

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